Chest computed tomography (CT) scan findings in patients with COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives Numerous cases of pneumonia of caused by coronavirus disease 2019 (COVID-19) were reported in Wuhan, China. Chest computed tomography (CT) scan is highly important in the diagnosis and follow-up of lung disease treatment. The present meta-analysis was performed to evaluate chest CT findings in COVID-19 patients. Materials and Methods All research steps were taken according to the MOOSE protocol and the final report was based on PRISMA guidelines. Each stage of the study was conducted by two independent authors. We searched the Web of Science, Ovid, Science Direct, Scopus, EMBASE, PubMed/Medline, Cochrane Library, EBSCO, CINAHL and Google scholar databases. The search was conducted on March 20, 2020. Grey literature was searched at medrxiv website. All analyses were performed using Comprehensive Meta-Analysis. The adapted Newcastle Ottawa Scale was used to evaluate the risk of bias. We registered this review at PROSPERO (registration number: CRD42019127858). Results Finally, 40 eligible studies with 4,183 patients with COVID-19 were used for meta- analysis. The rate of positive chest CT scan in patients with COVID-19 was 94.5% (95%CI: 91.7-96.3). Bilateral lung involvement, pure ground-glass opacity (GGO), mixed (GGO pulse consolidation or reticular), consolidation, reticular, and presence of nodule findings in chest CT scan of COVID-19 pneumonia patients were respectively estimated to be 79.1% (95% CI: 70.8- 85.5), 64.9% (95%CI: 54.1-74.4), 49.2% (95%CI: 35.7-62.8), 30.3% (95%CI: 19.6-43.6), 17.0% (95%CI: 3.9-50.9) and 16.6% (95%CI: 13.6-20.2). The distribution of lung lesions in patients with COVID-19 pneumonia was peripheral (70.0% [95%CI: 57.8-79.9]), central (3.9% [95%CI: 1.4-10.6]), and peripheral and central (31.1% [95%CI: 19.5-45.8]). The most common pulmonary lobes involved were right lower lobe (86.5% [95%CI: 57.7-96.8]) and left lower lobe (81.0% [95%CI: 50.5-94.7]). Conclusion Our study showed that chest CT scan has little weakness in diagnosis of COVID-19 combined to personal history, clinical symptoms, and initial laboratory findings, and may therefore serve as a standard method for diagnosis of COVID-19 based on its features and transformation rule, before initial RT-PCR screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".